---
title: "Domestic Well Vulnerability in Caliornia's Central Valley"
output: 
  flexdashboard::flex_dashboard:
    social: menu
    source: embed
---

Analysis {.storyboard}
=========================================

```{r setup, include=FALSE}
library(flexdashboard)
library(sf)
library(sp)
library(readr)
library(colormap)
library(leaflet)
library(here)

# load data from`06_calibration_herve.Rmd`
# b118cvsf      <- read_rds("b118cvsf.rds")
# gsacvsf       <- read_rds("gsacvsf_simp.rds") # simplified by rmapshaper
# domcv5ll_dry  <- read_rds("domcv5ll_dry.rds")
# domcv5ll_wet  <- read_rds("domcv5ll_wet.rds")
# icons_dry     <- read_rds("icons_dry.rds")
# icons_wet     <- read_rds("icons_wet.rds")
# 
# # public water system boundaries
# ws <- read_rds(here("data","public_water_systems",
#                     "service_areas_in_cv_sf.rds"))
```

### Climate change, drought, and the overexplotation of aquifers lower groundwater levels and put shallow domestic wells at risk of drying out.  {data-commentary-width=400}

```{r, out.width = '100%'}
knitr::include_graphics("img/motivation.png")
```

***

#### Background & Motivation

- California's Central Valley is the state's most agriculturally intensive region and heavily dependent on groundwater. It is also home to the state's largest population of domestic well reliant communities.  

- During the 2012-2016 drought, approximately **2,500** domestic wells failed in the Central Valley leaving tens of thousands of people without a reliable source of drinking water, which drew national attention and federal intervention.

- Hundreds of thousands upwards to a million Californians rely on domestic wells for drinking water.  

#### Questions

- How will a 1, 2, 3 or 4 year long drought affect domestic well failure in California's Central Valley?

- Are well failures more associated with particular social factors (i.e. - median income, ethnicity)? 

- Can machine learning models predict domestic well failure from climatological variables, and if so, what does climate change imply for domestic well vulnerability?


### Spatial and machine learning models driven by open data from public agencies were used to assess the vulnerability of domestic wells in California's Central Valley to failure. {data-commentary-width=400}

```{r, out.width = '100%', out.height= '100%'}
knitr::include_graphics("img/gwl2.gif")
```

***

#### Data & Approach 

- Seasonal groundwater level measurements *[left panel]* from the California Department of Water Resources (DWR) were used to interpolate water levels *[middle panel]* representing the shallow, to semi-confined Central Valley aquifer system.  

- Along with DWR well construction data on the location of domestic wells *[right panel]* and well pumps, a spatial model of well failure was built and calibrated to actual well failure in during the 2012-2016 drought.  

- As groundwater levels fall, shallow wells are more at risk of drying out than deep wells.  

```{r, out.width = '100%', out.height= '100%'}
knitr::include_graphics("img/cm.gif")
```


### Well failure during the 2012-2016 drought affectd the south east Tualre Basin, and northern parts of the Central Valley more than other locations. {data-commentary-width=300}

```{r}
b118cvsf  <- read_rds("data/b118cvsf.rds")
icons_dry <- read_rds("data/icons_dry.rds")
icons_wet <- read_rds("data/icons_wet.rds")
domcv5ll_dry <- read_rds("data/domcv5ll_dry.rds")
domcv5ll_wet <- read_rds("data/domcv5ll_wet.rds")

pal <- colorBin(palette = colormap(colormaps$viridis, nshades = 5),
                domain = b118cvsf$frp, bins = seq(0,50,10))

pal2 <- colorBin(palette = colormap(colormaps$jet, nshades = 10),
                 domain = b118cvsf$dry, bins = seq(0,600,60))

pal3 <- colorBin(palette = colormap(colormaps$jet, nshades = 5),
                 domain = b118cvsf$dens_100km2, bins = seq(0,5,1))

b118cvsf %>% 
  leaflet(width = "100%") %>% 
  addProviderTiles(provider = "CartoDB.Positron") %>%
  addPolygons(label = ~ paste(as.character(Subbasin_N), fc),
              # polygons
              fillColor = ~ pal2(dry), 
              fillOpacity = 0.7, 
              smoothFactor = 1,
              group = "Dry Well Count",
              # lines
              stroke = TRUE, 
              color = "#323232", 
              opacity = 1, 
              weight = 1) %>% 
  addPolygons(label = ~ paste(as.character(Subbasin_N), round(dens_100km2),2),
              # polygons
              fillColor = ~ pal3(dens_100km2), 
              fillOpacity = 0.7, 
              smoothFactor = 1,
              group = "Dry Well Density",
              # lines
              stroke = TRUE, 
              color = "#323232", 
              opacity = 1, 
              weight = 1) %>% 
  addPolygons(label = ~ paste(as.character(Subbasin_N), fc),
              # polygons
              fillColor = ~ pal(frp), 
              fillOpacity = 0.7, 
              smoothFactor = 1,
              group = "Failure Ratio",
              # lines
              stroke = TRUE, 
              color = "#323232", 
              opacity = 1, 
              weight = 1) %>% 
  addAwesomeMarkers(lng = domcv5ll_dry@coords[, 1],
             lat = domcv5ll_dry@coords[, 2],
             popup = paste("Well ID:", domcv5ll_dry$WCRNumber,"
",
                           "(", domcv5ll_dry$lon, "N", domcv5ll_dry$lat, "W)", "
",
                           "Pump Location:", round(domcv5ll_dry$pump_loc,2), "ft.", "
",
                           "Dry:", domcv5ll_dry$dry),
             icon = icons_dry,
             group = "Dry Wells",
             clusterOptions = markerClusterOptions()) %>%
  addAwesomeMarkers(lng = domcv5ll_wet@coords[, 1],
             lat = domcv5ll_wet@coords[, 2],
             popup = paste("Well ID:", domcv5ll_wet$WCRNumber,"
",
                           "(", domcv5ll_wet$lon, "N", domcv5ll_wet$lat, "W)", "
",
                           "Pump Location:", round(domcv5ll_wet$pump_loc,2), "ft.", "
",
                           "Dry:", domcv5ll_wet$dry),
             icon = icons_wet,
             group = "Active Wells",
             clusterOptions = markerClusterOptions()) %>%
  addLegend("topright", 
            pal = pal, 
            values = ~ frp,
            opacity = 1,
            title = "% Failure",
            group = "Failure Ratio",
            labFormat = function(type, cuts, p) {
              n = length(cuts)
              paste0(cuts[-n], " – ", cuts[-1], "%")
            }
            ) %>% 
  addLegend("bottomright", 
            pal = pal2, 
            values = ~ dry,
            opacity = 1,
            title = "Dry Well Count",
            group = "Dry Well Count",
            labFormat = function(type, cuts, p) {
              n = length(cuts)
              paste0(cuts[-n], " – ", cuts[-1])
            }
            ) %>% 
  addLegend("bottomleft", 
            pal = pal3, 
            values = ~ dens_100km2,
            opacity = 1,
            title = "Dry Well Density",
            group = "Dry Well Density",
            labFormat = function(type, cuts, p) {
              n = length(cuts)
              paste0(cuts[-n], " – ", cuts[-1], " per 100 sqkm.")
            }
            ) %>% 
  addLayersControl(overlayGroups = c("Failure Ratio", "Dry Well Count", "Dry Well Density", "Dry Wells", "Active Wells"), 
                   position = "topleft", 
                   options = layersControlOptions(collapsed = FALSE)) %>% 
  hideGroup(c("Dry Well Count","Dry Well Density","Dry Wells", "Active Wells")) %>% 
  addEasyButton(easyButton(
    icon="fa-globe", title="Zoom to Level 7",
    onClick=JS("function(btn, map){ map.setZoom(7); }"))) %>%
  addEasyButton(easyButton(
    icon="fa-crosshairs", title="Locate Me",
    onClick=JS("function(btn, map){ map.locate({setView: true}); }")))

```

***

#### Results: 2012-2016

- Spatial patterns in wells predicted to fail in the 2012-2016 dorught were very similar to wells that failed in the actual drought. The simulation's results are shown on in this interactive map. 

- Click points on the map to expand information on wells. 

- Red points indicate well failures, and blue points indicate wells that didn't fail.


### Future drought will affect areas with varying intensity. Another 4 year drought would impact twice as many domestic wells as the 2012-2016 drought, due to already low groundwater levels.  {data-commentary-width=400}

```{r,out.width = '100%', out.height= '100%'}
knitr::include_graphics("img/future_drought_pred-01.png")
# pd <- readr::read_rds("img/pred_1_2_3_4.rds")
# pd
```

***

#### Approach

- The calibrated spatial model (validated for the 2012-2016 drought) was used to predict future domestic well failure.

- 1, 2, 3, and 4 year long droughts were simulated by scaling the 4 year groundwater level change observed in 2012-2016 by factors of 0.25, 0.50, 0.75 and 1 respectively.

- The mean of Spring and Fall 2017 groundwater levels was used as the initial condition for groundwater level.

#### Results

```{r}
library(kableExtra)
library(knitr)

# create table of drought results
data.frame(drought_length = paste(1:4,c("yr","yrs","yrs","yrs")),
           n_well_failures = formatC(c(1282,2321,3370,4296), 
                                       big.mark = ",")) %>% 
  kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
```

- Across California's Central Valley alone, a **1-4 year drought** would result in **thousands** of domestic well failures, which would affect **tens of thousands** of people. 

- The southeastern Tulare Basin and northern basins are most susceptible to domestic well failure due to a combination of large water level declines and the prevalence of relatively shallow somestic wells.

- Due to differences in historical groundwater level decline, starting groundwater levels, and the distribution of well depths per region, some regions experience more than 10 times the density of well failure.

- Another 4 year drought would lead to as much as 13-14 well failures per 100 square kilometers in the most adversely affected regions.



### The 2012-2016 drought disproportionately affected low income communities. Low income communities are further from reliable sources of water. {data-commentary-width=400}



***

#### Are low income communities disproportionately affected by well failure?

The distances between each reported well failure during the 2012-2016 drought and the centroid of each Public Water System in the Central Valley was measured. Well failures were assigned a median income level determined by census tract.  

On average, low income communities are further away from a reliable source of drinking water than medium and high income communities.  

 




Affected Communities
=========================================

Column
-----------------------------------------

About
=========================================

This project was made by so and so.  

These are some of our methods.  

This is where we got data from.  

This is the github repo with all of the analysis scripts.  

We expect to write 2 peer-reviewed papers entitled: x and y.